Postdoctoral Research Associate in Smart and Green AI

About the Opportunity Position Overview Discipline Engineering Faculty Institute for Intelligent Networked Systems (INSI) Location London, Devon House (St Katherines Dock) Term 1.5 years, fixed term, full-time Salary Range Grade 7, 34 (£43,555 - £46,195 per annum), dependent on experience. Direct Reports N/A Reports to Professor Bipin Rajendran

The university supports staff maintaining a good work-life balance, offering: (i) flexible working and parental leave opportunities; (ii) an employee assistance programme which provides free, confidential advice on both home and work concerns,aswell as optional private medical insurance; (iii) season ticket loans; and(iv) beingpart of the cycle-to-work scheme. Start January 2027

• Contribute to the design, implementation, and evaluation of neuromorphic and in-memory hardware accelerator prototype boards for training and inference of state-of-the-art AI models.
• Develop algorithms, architectures, and hardware–software co-design methodologies for energy-efficient AI computation on neuromorphic and in-memory computing (IMC) platforms.
• Collaborate with cross-disciplinary teams spanning hardware design, AI/ML algorithms, information theory, and systems software to enable end-to-end system integration and validation.
• Publish high-quality research outcomes in leading journals and conferences in neuromorphic computing, AI hardware, and computer architecture.
• Support the supervision and mentoring of PhD and MSc students within the research group.
• Engage with industrial and academic collaborators to support joint research activities, technology transfer, and translational impact.
• Contribute to project management activities, including technical reporting, proposal development, and dissemination of research outcomes.

• PhD in Computer Science, Electronics or Computer Engineering or related quantitative field.
• Strong publication record in neuromorphic hardware design demonstrated through publications in leading conferences and/or journals, with expertise extending beyond materials and device optimisation toward system-, architecture-, and algorithm-level innovation.
• Experience in characterisation, modelling, and testing of nanoscale memristive and/or emerging non-volatile memory devices, including board-level integration and experimental evaluation of such devices.
• Experience in the simulation, implementation, and validation of state-of-the-art machine learning models, e.g., probabilistic, neuromorphic, Transformer, and/or state-space models, with an ability to translate algorithms into implementations suitable for emerging hardware platforms.
• Experience with FPGA-based test boards and hardware prototyping for emerging AI hardware systems, including the implementation and testing of algorithms on platforms incorporating emerging memory technologies such as STT-RAM and/or memristive devices.
• Experience in hardware–software co-design approaches aimed at mitigating, exploiting, or adapting to the non-ideal effects and variability of nanoscale devices, with an emphasis on developing robust algorithms and system-level solutions for real-world hardware.
• Effective communication (oral and written) skills, with the ability to write research reports and papers in a style accessible to academic audiences.
• Ability to work independently and as part of a team on research programmes.

The Intelligent Networked Systems Institute (INSI) at NU London is a newly established transatlantic research hub focused on next-generation connectivity and intelligent computing and learning systems. The INSI London Hub acts as a bridge between the US and Europe, advancing interdisciplinary collaboration across continents. INSI is dedicated…